Researchers and end users using climate data face a challenge when they analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often no longer possible. Most of the climate analysis tools for research and application needs must use very large datasets, often distributed among several data centres and into a large quantity of files. This is especially true when they are stored in a federated architecture like the ESGF. One of these tools is icclim (https://github.com/cerfacs-globc/icclim ), a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as CF, implementing also provenance information. It also aims at providing increasing support for all FAIR aspects. It is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R.Climdex as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html ). The new 5.x version of icclim is now based on functions from the xclim python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing. icclim is also a candidate as the software to calculate climate indices for the C3S toolbox (https://cds.climate.copernicus.eu/cdsapp#!/toolbox ). An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know if storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. These tools are integrated in the IS-ENES C4I 2.0 platform (https://climate4impact.eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ). Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like C4I enable the use of those tools by a larger number of researchers and end users. This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.

Recent improvements in GNSS capabilities of various Internet-of-Things (IoT) devices, including smartphones, have allowed for better PNT performance, as well as for new potential applications in geosciences. In particular, multi-constellation and multi-frequency receivers found in recent generations of smartphones bring great potential for GNSS science exploitation. However, access to IoT data for scientific purposes is currently limited, facing different data processing challenges. The project CAMALIOT: Application of Machine Learning Technology for GNSS IoT Data Fusion addresses these issues in order to increase the usability of GNSS IoT data for scientific purposes. It encompasses the whole pipeline from raw GNSS IoT data collection and development of methods for efficient and automatic processing, to the final suitability demonstration for determination and prediction of atmospheric parameters. In this way, the project will extend the capabilities of the GNSS Science Support Centre (GSSC) of ESA, which offers GNSS data and processing services for various domains. In order to collect raw GNSS IoT data, an Android app has been developed that will be the basis of a crowdsourcing campaign. The data ingestion, processing, and analysis are designed to be highly automated, robust, and scalable. Machine learning is used for several tasks in the processing scheme, including anomaly detection as well as data fusion and prediction. The GNSS IoT data is combined with geodetic GNSS data and external models and datasets related to the atmosphere and space weather to realize global grids of tropospheric parameters (zenith wet delays, gradients, precipitable water vapor) and ionospheric vertical total electron content. This work is funded by the NAVISP Element 1 program of ESA.

Numerical Weather Prediction models (NWP) have been used extensively since the ’40-’50s. Despite the advances in the field, the representation and forecast of the magnitude and variability of tropical processes in models is still a challenge. One of the steps to improve the precipitation forecasts using limited-area models is to evaluate which set of physical schemes and model domain configurations represent in a better way the actual behavior observed in the tropics. We implemented, as a part of a regional risk management strategy, two different operational weather forecast strategies for a complex terrain region in the Andes mountain range in northern South America. Both strategies, together, generate a total of eleven different forecasts every day, using the Weather Research and Forecasting model (WRF) with initial and boundary conditions from the Global Forecast System (GFS). The first configuration, implemented over five years ago and referred to as SYNAPSIS, includes three nested domains (18, 6 and 2 km) and is carried out every day using the 12 UTC GFS run and three different microphysics parametrizations: Eta Ferrier scheme, Purdue Lin Scheme and Thompson Scheme. The forecast lead-time of the latter strategy is 120 hours, and it does not use data assimilation. Since December of 2017, we implemented a second configuration termed RDFS, with two nested domains (12 and 2.4 Km), which carried out four times a day using the 00, 06, 12 and 18 UTC GFS runs. This configuration has a 30-hours lead time with the Thompson microphysics scheme. In RDFS, two WRF forecast runs are performed for each start hour, one assimilating weather radar reflectivity and the other without assimilation as control run, for a total of eight forecast runs daily. In this study, we assess the rainfall and temperature forecasts for all the different configurations using precipitation derived from reflectivity from weather radar, and air temperature at 2m from a network of automatic weather stations. We use 6 hourly and monthly skill scores (RMSE, BIAS, and Correlation coefficient) to quantify the precipitation differences between the SYNAPSIS and the RDFS configurations. To evaluate the impact of data assimilation in the precipitation forecast, we aggregate the results in a region within the inner domain, and then we calculate the average precipitation forecast between 0 and 36 predicted hours for RDFS with and without data assimilation. The results suggest a strong relationship between the forecast start time and the improve of precipitation forecast accuracy using data assimilation. The diurnal cycle of precipitation in the study region has a minimum in the morning (12 UTC) and a maximum in the afternoon (00 UTC) and during the night (09 UTC). The correspondence between the forecast improvement using data assimilation and the diurnal cycle of precipitation is likely due to the amount of assimilated data. In order to quantify the precipitation differences between the diffe

Data collected by space probes and Earth-based observations allow to investigate multiple aspects of planetary bodies in the Solar System. Typical spatial data utilized for geological and geophysical global scale investigations include imagery basemaps, digital elevation models, feature density maps, geochemical, magnetic and gravity anomaly maps. Different datasets often present local correlations, and their simultaneous use can help unveil the wide diversity of geological and geophysical processes that contribute to the planetary evolution. A geostatistical approach can be applied to quantify the level of correlation and anticorrelation between the different datasets. We developed a method based on a linear regression analysis of multiple planetary data, producing georeferenced maps that can be imported into a Geographic Information System (GIS) environment. We show how this geostatistical method can be used to quantify the level of correlation between different spatial data, and to investigate the geological and geophysical processes of planetary bodies. We present the results of the geostatistical method for the Mercury science case, using datasets returned by the MESSENGER mission.

MAJOR POINTS: 1) The Barents and GIN basins are warming rapidly and exhibiting strong strong interannual variability. 2) While interannual variability of heat content in the GIN Seas is controlled by Atlantic water transport, we show interannual variability of heat content in the Barents is controlled by surface flux (see also Schlichtholtz et al 2011). 3) Interannual variations in surface flux are the result of variations in wintertime latent heat loss resulting from intrusions of warm moist air from the south. 4) (not shown) these variations also control winter sea ice cover.

Accelerating demand for energy storage has led to increasing development of brine resources in the “Lithium Triangle”, estimated to hold about 75% of the planet’s Li reserves but persistent and fundamental questions regarding the source and transit time of groundwater have confounded efforts to manage these resources effectively. The basins containing these brines lie within the massive Altiplano-Puna Plateau, home to people whose ancestors have inhabited this land for thousands of years and fragile ecosystems that exist nowhere else on Earth. This region is very dry, bordering Earth’s driest non-polar desert and as such, groundwater is the predominant and, in many areas the only source of water. Fundamental questions about the spatiotemporal dimensions of these groundwater systems have only begun to be addressed. In much of this extreme and remote region, there is a severe lack of quality baseline understanding of the regional hydrological system and connections between surface and groundwater bodies. To address these questions, we utilize an exhaustive set (~2,500 individual analyses) of environmental tracer data (δ18O, δ2H, 3H, 87Sr/86Sr), and dissolved major and minor elements in waters collected from over a dozen field campaigns in the Salar de Atacama and Altiplano of Chile and on the Puna Plateau of Argentina. Our integrated analysis pairs these data with rigorous geochemical modelling and physical hydrological measurements from the field and remote sensing products. 3H data show much of the groundwater currently discharging into these basins is non-modern (>60 yrs. old), stable isotope and geochemical data show strong connectivity but also a marked disconnect between some recharge and discharge areas. We show that “fossil” groundwater, 100-10,000 yrs. or older is widespread and fundamental to the system, sharp disconnects exist between the modern hydrological system, the water bodies it sustains, and those sustained by paleo-recharge water. By defining these connections in spatial detail and within a regional integrated framework, we greatly improve the fundamental mechanistic understanding of this and other groundwater-sustained systems. This will greatly improve the ability of communities, governments and industry to manage of these water resources in a way that is genuinely sustainable.

The National Antarctic Data Center (NADC) is the ICT infrastructure designed to gather, handle, publish and provide access to the large amount of scientific data collected by several projects in the framework of the Italian Antarctic National Research Program (PRNA). Aim of the infrastructure is to provide a single integrated system that allows the final users to easily access and share data wherever they are stored. The architecture is based on a System-of-Systems (SoS) concept: a set of systems (functional nodes) interconnected together with each other by means of mediation and adaptation services running on a central infrastructure (common node). The common node is managed by the five Organizations (CNR, INGV, ENEA, OGS, MNA) that contribute to the NADC and is devoted to a regular harvesting of the metadata. Each functional node consists of an existing metadata and data management system implemented by each Organization. Istituto Nazionale di Geofisica e Vulcanologia (INGV) hosts one of those functional nodes and it is managing, among others, data/metadata produced by the permanent geomagnetic and ionospheric observatories installed in Antarctica since 1985. The functional nodes are interconnected and federated together by means of interfaces and standard data/metadata models. This distributed architecture allows to interconnect heterogeneous systems and digital infrastructures in a flexible, scalable and sustainable way. This paper describes the general infrastructure and, as an example of functional node, the contribution of the data management related to the Antarctic Ionospheric and Geomagnetic Observatories managed by INGV at Mario Zucchelli station (74°41′42″S, 164°06′50.4″E) and Concordia base (75°05′59.91″S, 123°19′57.38″E).

Internal waves and vortical mode are ubiquitous in the ocean, providing a conduit for energy transfer from tides and wind-driven circulation to fine- and micro-scale turbulence associated with internal-wave breaking and dissipation. Numerical simulations of a Garrett-Munk internal-wave field are used to study the interactions between internal waves and vortical mode, and the generation of turbulent mixing events and semi-permanent finestructure (vortical mode). Regions of reduced stratification are found to occupy between ~5% and 25% of the model domain, depending on the strength of the anomalies. Decomposition of 3-dimensional model fields into linear internal-wave and vortical-mode components shows that both influence stratification, with ~88% of the anomalies due to linear internal waves and ~40% due to vortical mode, the overlap in percentiles representing regions that are a combination of the two. The time evolution of anomalies is examined, from the initial generation to eventual dissipation. Results further describe the preconditions of these regions, including their relation to a subcritical Richardson number and overturning / mixing events as quantified via Thorpe scales and turbulent dissipation rate.

Garnet-bearing volcanic rocks are extremely rare at convergent margins, with few known occurrences worldwide [ref 1-2]; however, such rocks are common within the late Miocene volcanic rock suite of the Northern Andean block (NAB) along the Central Cordillera, Colombia [ref 3-5]. They have been linked to pre-existing zones of crustal weakness that channeled magmas to the upper crust in a short period of time [ref 5-6]. Here we present new geochronological and petrographic data to constrain the timing and petrogenesis of these unusual rocks. We obtained mineral chemical analyses from 7 porphyritic-andesite samples from the eastern flank of the Central Cordillera and the Cauca-Patia Basin, Colombia. Garnet phenocrysts are almandine in composition, ranging from 23 to 29 wt.% FeO, 6 to 8 wt.% CaO, and 1 to 4 wt.% MnO. In some samples, garnets are homogeneous with no reaction rims and lacking inclusions; however, in other samples, garnets show re-absorption rims and inclusion assemblages similar to the rock matrix (plag, amph, ± bt) as well as rare plagioclase coronas. The high Ca and low Mn contents of the NAB garnet cores are consistent with crystallization at ~1.2GPa, based on phase equilibrium experiments of [ref 7], while garnet rim assemblages are congruent with a second stage of crystallization at ~0.8GPa under water-undersaturated conditions. Finally, a pre-eruption dehydration stage is evidenced by the presence of breakdown rims in amphibole crystals. Our new U-Pb in zircon ages reveal that NAB garnet-bearing volcanic rocks formed between 9 and 8 Ma. Taken together, our data suggest a rapid ascent of the NAB magmas associated with the onset of regional volcanism and extension, and possibly the development of the Caldas Tear, a slab window within the Nazca Plate. [1] Green & Ringwood (1968) CMP. [2] Harangi et al. (2001) Journal of Petrology. [3] Orrego (1975) UNAL Colombia. [4] García (1983) UNAL Colombia. [5] Bissig et al. (2017) EG. [6] Weber et al. (2018) SGC (in press). [7] Alonso-Pérez et al. (2006) CMP.

Conventional tidal prediction models typically combine constituent lunisolar forcing factors harmonically fit to sets of collected tidal gauge data. A harmonics analysis is favored over a precise orbital ephemeris-based gravitational forcing model, as tides are localized in scope and sensitive to a particular volume geometry. But what happens when the dynamic behavior is much larger in scale? As we have demonstrated previously [1], lunisolar tidal constituents forced by a strong biennial-modulated annual signal will provide a high-quality fit to ENSO – albeit subject to over-fitting of the numerous constituent factors available. Yet as ENSO is a large-scale phenomenon, it should be more amenable to applying a precise set of ephemeris data as the forcing to a Laplace’s tidal equation formulation. This should reflect the underlying physics governing the dynamics more realistically, while severely constraining the degrees of freedom (DOF) in factors which lead to the possibility of over-fitting. We used the NASA JPL Horizons (https://ssd.jpl.nasa.gov/horizons.cgi) ephemeris data for the Sun and Moon as a parametric input to the well-known 1/R^3 gravitational forcing function and verify as good a quality fit as that available from a high-DOF harmonics approach. This extends over the modern-day instrumental record of ENSO but also covers the coral proxy records that span the years from 1650 to 1880. The approach works effectively because the extra DOF (including phases and elliptical nonlinearities in the orbits) needed to precisely define the gravitational forcing are accurately tracked by the Horizons ephemeris algorithm. Importantly, the results are highly sensitive to the relative forcing amplitudes, which is not surprising, since the fast lunisolar cycles are projected over spans of hundreds of years. The challenge is equivalent to attempting to perform a conventional tidal analysis over a similarly lengthy time span, while also dealing with noise and a limited resolution time-series. [1] Pukite, P.R. “Biennial-Aligned Lunisolar-Forcing of ENSO: Implications for Simplified Climate Models.” AGU Fall Meeting, 2017.

The production of specialty crops in the Pacific Northwest is an economically important industry and one that is likely to be heavily impacted by rising temperatures and decreasing snowpack associated with climate change. The purpose of this study was to interview small-scale specialty crop farmers in Oregon's Willamette Valley and Central Coast Range to understand how they are experiencing climate change and what information they are using to make farm-management decisions related to the climate. Semi-structured interviews were conducted with seven specialty crop farmers on farms in Corvallis, OR or at the Corvallis Farmers' Market. The farmers believe that factors such as high temperatures earlier in the growing season, hotter temperatures, increased weather variability, and decreased precipitation are impacting their farms. The interviewed farmers employ multiple strategies to manage the risk of their work, which include incorporating heterogeneity and flexibility into their management, using irrigation and sheltering infrastructure to control the microclimates for crops, and selecting different crops or crop varieties. When considering the information sources farmers use when making decisions about climate and weather, some farmers use short-term weather forecasts, but none rely on longer-term forecasts. As the farmers in this study are all concerned about climate change, having accessible information for the upcoming season and future climate could be helpful if they believe that long-term forecasts are credible.

Decision makers and water managers throughout New England desire to understand how development and land-use change, especially under a changing climate, will affect high flows, flooding, and stormwater. We use the New England Landscape Futures (NELF) project to demonstrate the potential for translating participatory scenarios to simulations of land use and land cover and the resulting effects on streamflow. In addition to projecting recent trends, four other landscape scenarios were co-designed through a structured process that engaged over 150 stakeholders and scientists from throughout New England. Daily streamflows were simulated with the Soil and Water Assessment Tool (SWAT) to investigate how high flows vary among the scenarios for the less developed Cocheco River watershed in southeast New Hampshire and the more urbanized Charles River watershed in eastern Massachusetts. The hydrologic response of each land-use scenario was simulated for both historic weather (1999-2017) and downscaled weather for 2049-2067 from the CCSM 4.0-RCP 8.5 model-pathway from the Community Earth System Model. Differences among the land-use scenarios led to no differences in average annual water yield and ET. Loss of forest and increase in urban area reduces the baseflow contribution to streamflow while increasing storm runoff. This shift in partitioning did not affect the frequency of high flows (5% exceedence). The increase in runoff did lead to a concomitant increase in the average annual maximum flow, and the effect is larger in the Charles River watershed than in the Cocheco. Under the future climate, a combination of increased precipitation and decreased potential evaporation results in increased streamflow relative to the scenarios modeled with the historic weather. As a fraction of precipitation, surface runoff remains the same, and baseflow increases. The frequency of high flows increases, with the 5%-exceedence flow (under historical weather) being met or exceeded 8-9% of the days. Annual maximum flows also increased for the future climate, and the effects of land-use change and climate on annual maximum flows are comparable. These water-related results fit into a larger framework for evaluating ecosystem services associated with socially relevant landscape scenarios.

We develop a Hamiltonian Monte Carlo (HMC) sampler which solves a multi-parameter elastic full-waveform inversion (FWI) in a probabilistic setting for the first time. This gives novel access to the full posterior distribution for this type of highly non-linear inverse problem. Typically, FWI has focused on using gradient descent methods with proper regularization to iteratively update models to a minimum misfit value. Non-uniqueness and uncertainties are mostly in this approach. Bayesian inversions offer an alternative by assigning a probability to each model in model space given some data and prior constraints. The drawback is the need to evaluate a very large number of models. Random walks from Markov chains counter this effect by only exploring regions of model space where probability is significant. HMC method additionally incorporates gradient information, i.e. local structure, typically available for numerical waveform tomography experiments. So far, HMC has only been implented for acoustic FWI. We implement HMC for multiple 2D elastic FWI set-ups. Using parallelized wave propagation code, wavefields and kernels are computed on an regular numerical grid and projected onto basis functions. These gradients are subsequently used to explore the posterior space of different target models using HMC. The free parameters in these experiments are P and S velocity, and density. Although simulating Hamiltonian dynamics in the resulting phase space is approximated numerically, the results of the Markov chain are nevertheless very insightful. No prior tuning of kernels, data or model space is required, under the constraint that the sampler is properly tuned. After a burn in phase during which the mass matrix is iteratively optimized, the Markov chain is run on multiple nodes. After approximately 100,000 samples (combined from all nodes) the Markov chain mixes well. The resulting samples give access to the full posterior distribution, including the mean and maximum-likelihood models, conditional probabilities, inter-parameter correlations and marginal distributions.

Mercury is a notorious pollutant causing severe nerve diseases. This metal has a semi-volatile nature under the STP condition, therefore, it is emitted to atmosphere as gaseous elemental mercury (GEM) from variety of sources, such as the ocean surface, volcanoes, and fossil fuel combustion. Because of the long atmospheric life time, mercury is transported for long distance and spreads globally, consequently ubiquitous in the natural environment. The current global model for mercury cycle assumes that oxidation is a predominant pathway for the removal of GEM from the atmosphere. To the best of our knowledge, however, none of heterogeneous uptake of GEM by atmospheric water, such as cloud, fog, and rain droplets, has been evaluated to date. For more accurate understanding of the mercury cycle, the evaluation of this process is necessary. We conducted laboratory experiments to evaluate GEM uptake by artificial atmospheric water. The experiments were carried out using a 3 L glass cell. 500 mL of acidified water was transferred into the cell, and approximately 50 to 10 ng m-3 of GEM gas mixture was continuously flowed into and out of the open space above the solution in the cell. After a definite time the solution was quantitatively analyzed for mercury to investigate the uptake. At this presentation preliminary results of the uptake experiments will be presented.

To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we develop a spatially and temporally high resolution polar regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM–SMAP). At present, the horizontal resolution of the model is set to be 5 km with the temporal interval of 1-hour. The model forced by the Japanese 55year Reanalysis (JRA-55) has been evaluated in the GrIS during the 2011–2014 mass balance years using in-situ surface meteorological and stake measurement data [1]. Results show that the model reproduced the measured features of the GrIS climate, diurnal variations, and even a meso-scale strong wind event. In particular, it reproduces the GrIS surface melt area extent well. Recently, near real-time calculations with the model are conducted operationally. Here we introduce the concept and framework of the model, then present performance of the model from various aspects. In addition, some new results from the operational near-real time calculations will be shown and discussed. Reference [1] Niwano et al. (2018) NHM-SMAP: Spatially and temporally high resolution non-hydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet. The Cryosphere, 12, 635-655, doi:10.5194/tc-12-635-2018.

Water security hinges on water storage. Although the public and water resources planners habitually look to surface reservoirs for storage solutions, by far the largest ‘space’ to store water is underground. The very nature of freshwater distribution on Earth foreshadows future water storage solutions, as 97% of all circulating freshwater globally is in groundwater. Similarly, although 140 surface reservoirs in California can store 52 km3(42 MAF), in the Central Valley aquifer system there is room for another ~170 km3(~140 MAF) owing to past depletion. Despite the state’s Mediterranean climate in which nearly all of the precipitation occurs between November and March when demand is lowest, historically massive snow storage and spring-summer snow melt synchronized well with surface reservoir replenishment during April-July. This system built around snow storage as a means of mitigating winter flood threats and delaying runoff until the beginning of the peak demand season is clearly demonstrating significant vulnerabilities to climate change and drought. Climate warming has already produced decades of declining snowmelt runoff, making surface reservoir storage more difficult. Moreover, as demonstrated during the 2012-16 drought, in the face of droughts longer than a few years, the surface storage offers inadequate long-term water security. This fact, the fact that California during pre-development times of the last millennium experienced far longer droughts, and ongoing climate change clearly indicate the need for a different strategy that more fully leverages both surface and subsurface storage. Kocis and Dahlke (2017) show that increasing winter runoff during wet and normal years provide enough high-magnitude flows to support a strategy of diverting flood flows for groundwater storage. This “flood-MAR” (managed aquifer recharge) approach will require a massive change in winter water and land management that exploits recharge opportunities on irrigated farm lands and in areas with suitable soils and subsurface geology. A case study in the American-Cosumnes Rivers portion of the Central Valley shows how total system water storage can be increased dramatically through diversion of high-magnitude flows and reoperation of both the surface and subsurface reservoirs including economic incentives.

Climate change is forcing the ski industry to modify snow-making strategies and facility operations. Over-summer snow storage is an adaptation successfully employed by high-elevation and/or high-latitude ski centers in Europe, Canada, and Asia. The process involves stockpiling winter snow and storing it beneath insulation (e.g., wood chips) through summer. Current methods are empirically-based with few studies quantifying snowmelt through summer or comparing insulation strategies. In this project, we evaluate the feasibility of over-summer snow storage in Vermont, northeastern North America. Soil temperatures were recorded since June 2017 with sensors 5, 20, 50 cm and 1 m below the ground surface. In March 2018, two, 200 m3 snow piles were covered in plastic and wood chips; we monitored their volume bi-weekly through the melt season using terrestrial LiDAR. We also measured air to snow temperature gradients under various insulation materials: rigid foam, open cell foam, and wet wood chips, all with and without reflective coverings. Away from snow piles, ground temperatures at 1 m depth were ~7C in spring 2017, rising to 12C in summer, and falling to just above 0C in winter. As depth decreased, ground temperature became more responsive to air temperature; ground temperature lagged air temperature at all depths. Below summer snow piles, soil temperature at all depths remained near freezing through the summer as cold meltwater percolated into the ground. Snow was lost from each pile at a similar rate (~1.3 m3 day-1) from late March to mid-June; melt then accelerated slightly in response to increased air temperature, solar radiation, and humidity. Large crevasses formed in both piles along the edge of the plastic sheeting which exposed snow to direct sunlight. Temperature was at or above 10C over the snow below both rigid foam and open-cell foam with a strong diurnal variation, regardless of the addition of a reflective blanket. Beneath wet wood chips covered with a reflective blanket, temperature remained close to freezing even though air temperature was > 30C. There was no diurnal variation, indicating that wood chips effectively buffered thermal swings. It appears that a reflective surface over >20cm of wet wood chips is most effective at minimizing summer snow melt in humid, northeastern North America.

The Earth’s crystalline inner core (IC) solidifies from the liquid Fe alloy of the outer core (OC), which releases latent heat and light elements sustaining the geodynamo. Variability in solidification regime at the inner core boundary (ICB) may result in compositional and thermal multi-scale mosaic of the IC surface and dissimilarity of its hemispheres. Both the mosaic and hemisphericity are poorly constrained, not least due to a lack of available sampling by short-period reflected waves. Measured amplitude ratio of seismic phases of PKiKP and PcP reflected, respectively, off the inner and outer boundary of the liquid core, yields direct estimate of the ICB density jump. This parameter is capable of constraining the inner – outer core compositional difference and latent energy release, but is not well known (0.2–1.2 g/cub cm), and its distribution is obscure. Travel time measurements of PKiKP and PcP waveforms can be useful in terms of getting an insight into fine structure of ICB and its topography. We analyse a new representative sample of pre-critical PKiKP/PcP differential travel times and amplitude ratios that probes the core’s spots under Southeastern Asia and South America . We observe a statistically significant systematic bias between the measurements collected in western and eastern hemispheres, and carefully examine its origin. Separating the effects of core-mantle boundary (CMB) and ICB on the measured differentials is particularly challenging and we acknowledge that a whole class of physically valid models involving D” heterogeneities and lateral variation in lower mantle attenuation can be addressed to account for the observed hemisphericity. However, we find that variance in PKiKP-PcP differential travel times measured above the epicentral distance of 16 degrees is essentially due to mantle heterogeneities. Analysis of data below this distance indicates the ICB density jump under Southeastern Asia can be about 0.3 g/cub. cm, which is three times as small as under South America where also the thickness of the liquid core can be by 1-3 km in excess of the one in the East. The findings are interpretable as evidence for IC hemispherical asymmetry whereby crystallization dominates in the West and melting in the East (and not vice versa) or in terms of two disconnected mosaic patches with contrasting properties.